Addressing Trust in AI Systems through Education: A Didactic Perspective

The paper identifies two main challenges in machine learning education: opaque tools that give learners only a superficial grasp of ML and hinder the development of calibrated trust in AI systems. It introduces ICE‑T, a didactic framework that combines intermodal transfer, computational thinking, and explanatory thinking to provide representational richness, graduated process control, and error contextualization. By linking these facets to trust‑calibration literature, the authors argue that trust should be an explicit educational goal and that ICE‑T offers a scalable method to achieve it.

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